System and method for improving workflow efficiencies in reading tomosynthesis medical image data

ABSTRACT

A system and a method are disclosed that forms a novel, synthetic, two-dimensional image of an anatomical region such as a breast. Two-dimensional regions of interest (ROIs) such as masses are extracted from three-dimensional medical image data, such as digital tomosynthesis reconstructed volumes. Using image processing technologies, the ROIs are then blended with two-dimensional image information of the anatomical region to form the synthetic, two-dimensional image. This arrangement and resulting image desirably improves the workflow of a physician reading medical image data, as the synthetic, two-dimensional image provides detail previously only seen by interrogating the three-dimensional medical image data.

RELATED APPLICATIONS

This Application is a continuation of co-pending U.S. patent applicationSer. No. 13/684,475, filed Nov. 23, 2012, entitled SYSTEM AND METHOD FORIMPROVING WORKFLOW EFFICIENCIES IN READING TOMOSYNTHESIS MEDICAL IMAGEDATA, the entire disclosure of which is herein incorporated byreference.

BACKGROUND

1. Field of the Invention

This application relates generally to image processing for biomedicalapplications. More particularly, this application relates to improvingworkflow efficiencies in reading medical image data.

2. Description of the Related Art

In the fields of medical imaging and radiology, various techniques maybe employed for creating images of an anatomical region of the humanbody. For example, in mammography, the breast is often imaged at twofixed angles using x-rays. Physicians may review two-dimensional (2D) orplanar x-ray images of the anatomical region to uncover and diagnosedisease-like conditions, such as breast cancer.

Numerous medical imaging procedures now employ systems and techniquesthat create three-dimensional (3D) or volumetric imagery of the humanbody. For example, significant attention has been given to tomographicimaging techniques. One such example is digital breast tomosynthesis(DBT), a relatively new imaging procedure in which systems image abreast by moving a source and exposing the breast to radiation from aplurality of angles, thus acquiring high resolution, planar images(i.e., “direct projections”) at different angles. For example, a DBTsystem may acquire 10 direct projection images in which the source movesin such a way as to change the imaging angle by a total angle of 40degrees.

3D medical images enable physicians to visualize important structures ingreater detail than available with 2D medical images. However, thesubstantial amount of image data produced by 3D medical imagingprocedures presents a challenge. In mammography, for example, aphysician may review two images of a breast: a cranial-caudal (CC) imageand a medial-lateral oblique (MLO) image. In DBT, the physician mayreview approximately 50-70 images, which could include the originalprojection images and reconstructed images.

Several techniques for improving the speed of diagnostic assessment aredisclosed in U.S. Pat. No. 7,630,533, entitled BREAST TOMOSYNTHESIS WITHDISPLAY OF HIGHLIGHTED SUSPECTED CALCIFICATIONS; U.S. Pat. No.8,044,972, entitled SYNCHRONIZED VIEWING OF TOMOSYNTHESIS AND/ORMAMMOGRAMS; U.S. Pat. No. 8,051,386, entitled CAD-BASED NAVIGATION OFVIEWS OF MEDICAL IMAGE DATA STACKS OR VOLUMES; and U.S. Pat. No.8,155,421, entitled MATCHING GEOMETRY GENERATION AND DISPLAY OFMAMMOGRAMS AND TOMOSYNTHESIS IMAGES, the teachings of which patents areincorporated herein by reference as useful background information.However, solutions are desired that would further improve the speed ofdiagnosis without sacrificing the detail provided by 3D medical imagingtechnology.

SUMMARY OF THE INVENTION

This invention overcomes disadvantages of the prior art by providing asystem and method for improving workflow efficiencies in readingtomosynthesis medical image data that avoids sacrificing desired detailin images. The system and method generally enhances the identificationof regions and/or objects of interest (ROIs), such as masses, within anacquired image by performing, based on three-dimensional (3D) data, anenhancement process to the image before it is projected into atwo-dimensional (2D) format. This renders the regions/object(s) ofinterest more identifiable to a viewer (e.g. a diagnostician, such as aphysician and/or radiologist) in the 2D-projected image as it boundariesare more-defined within the overall field.

In an illustrative embodiment, the system and method acquires, using anacquisition process, one or more two-dimensional (2D) regions ofinterest (ROIs) from a three-dimensional (3D) medical image of ananatomical region. The medical image is obtained from a scanning processcarried out on a patient by an appropriate medical imaging device andassociated data handling and storage devices. A first projection processdefines a first 2D projection image of the anatomical region. Then, asecond projection process generates a second 2D projection image of theanatomical region using image information from the first 2D projectionimage and the one or more 2D ROIs. The second 2D projection image isthen output to be stored and/or displayed using an appropriate storagesystem and/or display device. The second projection process can beconstructed and arranged, in a blending process, to blend the one ormore 2D ROIs with image information from the first 2D projection image,and can include an ROI detector that forms at least one ROI responseimage. The blending process can be further constructed and arranged toextract 2D binary masks of the one or more ROIs from at least one ROIresponse image and/or to blend the 2D binary masks with the first 2Dprojection image to generate the second 2D projection image.Additionally, a three-dimensional response image based upon a selectedportion of the second 2D projection image can be provided to assist thediagnostician in identifying a region or object of interest, such as amass. This 3D response image characterizes the degree to which variouspoints or regions in an image exhibit characteristics interest.

BRIEF DESCRIPTION OF THE DRAWINGS

Various inventive embodiments disclosed herein, both as to itsorganization and manner of operation, together with further objectivesand advantages, may be best understood by reference to the followingdescription, taken in connection with the accompanying drawings as setforth below in which:

FIG. 1 is a block diagram of a medical imaging system according to anillustrative embodiment;

FIG. 2 is a flow diagram of an illustrative image processing processthat can be performed by the medical imaging system of FIG. 1;

FIG. 3 is a flow diagram of an illustrative process for using a regionof interest (ROI) enhanced two-dimensional image to improve theefficiency with which a viewer/diagnostician (physician, radiologist,etc.) reads medical image datasets;

FIG. 4 is a display image of an exemplary 2D projection containing anobject of interest without processing according to the illustrativeembodiment; and

FIG. 5 is a display image of an exemplary 2D projection containing theobject of interest of FIG. 4 after enhancement processing according tothe illustrative embodiment.

DETAILED DESCRIPTION

FIG. 1 is a block diagram of a medical imaging system 100 in accordancewith an illustrative embodiment. The system includes a three-dimensionalmedical image source 110, a two-dimensional medical image source 116,and an image processing unit 120 that produces a novel, region ofinterest (ROI)-enhanced two-dimensional image 140 that can be theprimary image read for detection and diagnosis of disease by adiagnostician. The system 100 further includes a graphical userinterface (GUI) and/or display 142 for outputting the various medicalimage data. It should be noted that a wide range of functionalcomponents can be provided to the system, 100 in various embodiments,including various networked data-handling and storage devices,additional displays, printing devices, interfaces for portable computingdevices, etc.

According to an embodiment, the three-dimensional medical image source110 is a digital tomosynthesis imaging system such as offered by theGeneral Electric Company of Fairfield, Conn. (GE); Hologic, Inc, ofBedford, Mass. (Hologic); or Siemens AG of Munich, Germany (Siemens).Digital tomosynthesis imaging systems image an anatomical region bymoving a source, and acquiring a plurality of projection images (e.g.,10-25 direct projections) at different angles (e.g., at 4-degreeincrements).

As illustrated in FIG. 1, the three-dimensional medical image source 110provides a three-dimensional image 112 of an anatomical region 114.According to an embodiment, after the source 110 acquires projectionimages, the projection images are input to a reconstruction processingunit, which employs conventional techniques and processes to constructan image volume of the anatomical region. By way of one example, theimage volume can be constructed in 40-60 image thin slices, each thinslice having a spatial resolution of 100 microns per pixel, a thicknessof 1 millimeter (mm), and dimensions of 2500 rows of pixels by 1500columns of pixels.

According to an embodiment, the two-dimensional medical image source 116provides a two-dimensional image 118 of the anatomical region 114. Byway of one example, source 116 can include a computer memory ofconventional design that reads the image 118 from a disk or other datastorage device. The depicted source can be defined to include associatedstorage hardware in such embodiments. By way of another example, source116 can be defined to include a tomosynthesis image acquisition unitcapable of operating in a full-field digital mammography imaging modeand acquiring medio-lateral oblique (MLO) or cranio-caudal (CC)two-dimensional images. By way of yet a further example, source 116 canbe defined to include image processing computer software capable ofsynthetically producing two-dimensional images from existing image dataof the anatomical region 114.

Note, as used herein the terms “process” and/or “processor” should betaken broadly to include a variety of electronic hardware and/orsoftware based functions and components. Moreover, a depicted process orprocessor can be combined with other processes and/or processors ordivided into various sub-processes or processors. Such sub-processesand/or sub-processors can be variously combined according to embodimentsherein. Likewise, it is expressly contemplated that any function,process and/or processor here herein can be implemented using electronichardware, software consisting of a non-transitory computer-readablemedium of program instructions, or a combination of hardware andsoftware.

The image processing unit 120 further includes a three-dimensional ROIdetector 124, a two-dimensional ROI extractor 128, and an image blendingunit 132.

The three-dimensional ROI detector 124 characterizes the degree to whichvarious points or regions in an image exhibit characteristics ofparticular interest. For example, characteristics that may be ofinterest in a breast include blob-like regions or spiculated regions,both of which could indicate malignancy. Thus, according to anembodiment, the detector 124 can include a calcification detector, blobdetector, a spiculation detector, or combinations thereof. Asillustrated in FIG. 1, the three-dimensional ROI detector 124 producesan ROI response image 126 that contains this characterizationinformation for every image slice in the three-dimensional image 112.

The two-dimensional ROI extractor 128 extracts two-dimensionalinformation from portions of the three-dimensional image 112 thatinclude the points or regions of interest exhibiting the characteristicsof interest. According to an embodiment, the extractor 128 extracts a 2Dbinary mask 130, also referred to herein as a chip 130, for each ROI.

According to an embodiment, the image blending unit 132 includes ablending function or process that combines the two-dimensionalinformation extracted by the extractor 128 with the two-dimensionalimage 118 provided by source 116. The blending function/process formsthe ROI-enhanced two-dimensional image 140.

FIG. 2 is a flow diagram of the operational image processing that can beperformed by the medical imaging system 100 to produce an ROI-enhancedtwo-dimensional image.

At a step 210, a three-dimensional, reconstructed image volume of ananatomical region is acquired from the three-dimensional image source110.

At a step 220, the three-dimensional ROI detector 124 processes the 3Dreconstructed image volume of the anatomical region to form the ROIresponse image 126.

At a step 230, the ROI extractor 128 extracts 2D binary masks of ROIsfrom the ROI response image 126. According to an embodiment, the ROIextractor 128 first finds the local maxima of ROIs in the responseimage. A local maximum specifies the 2D slice of the three-dimensionalimage from which the binary mask should be optimally extracted. Then,the ROI extractor 128 extracts the 2D binary mask of the ROI bythresholding the response image. In one embodiment, the threshold valueto be applied is a fixed variable whose value can be set using empiricaldata. Finally, the ROI extractor 128 performs a mathematicalmorphological dilation operation to ensure that the extracted 2D binarymask will encompass the entire structure of interest.

At a step 240, the image blending unit 132 blends each 2D binary maskinto the two-dimensional image 118. According to an embodiment, theblending unit 132 first computes a soft blending mask from the 2D binarymask, which will ensure that the ROIs are smoothly blended into thefinal image. An illustrative technique for computing the soft blendingmask involves applying a known Gaussian smoothing filter on the 2Dbinary mask. Then, the blending unit 132 performs the following blendingfunction:

For each pixel i in the mixed_image

mixed_image[i]=original_image[i]*(1−soft_mask_value[i])+chip_image[i]*soft_mask_value[i]

In this function, original_image[i] refers to the pixel intensity of thetwo-dimensional image 118, the soft_mask_value[i] refers to the pixelintensity in the soft blending mask, and the chip_image[i] refers to thepixel intensity in the 2D binary mask.

FIG. 3 is a flow diagram of an illustrative process in which system 100uses a region of interest (ROI)-enhanced two-dimensional image toimprove the efficiency with which a physician reads medical imagedatasets.

At a step 310, the system 100 outputs an ROI-enhanced 2D image to adisplay, such as the graphic user interface 142 described with referenceto FIG. 1.

At a step 320, the system 100 receives input specifying a spatial x, ycoordinate location in the 2D image. For example, the input can specifya point or region in the 2D image that is of further interest to thephysician/diagnostician.

At a step 330, the system 100 programmatically determinesthree-dimensional image information that would optimally aid thephysician's task of interpreting the specific point or region ofinterest. According to an embodiment, the system 100 utilizes athree-dimensional response image to make this determination. Aspreviously described, a three-dimensional response image characterizesthe degree to which various points or regions in an image exhibitcharacteristics of particular interest. The system 100 identifies theslice of the three-dimensional response image where the specifiedspatial point exhibits the local maxima (i.e., the point or region ofinterest is most blob-like, most spiculated, etc.)

At a step 340, the system 100 outputs the three-dimensional imageinformation that includes the spatial point exhibiting the local maximato a display. By way of one example, the system 100 outputs the specificslice identified in the previous step. By way of another example, thesystem 100 computes a slab image that includes the spatial point andoutputs the slab image to the display.

To again summarize, the illustrative system and method effectivelyincreases the efficiency of a physician/diagnostician (e.g. radiologist)in reading tomography images. Typically, reviewing the 3D data istime-consuming and labor-intensive for such personnel. Specifically, inthis modality, masses are visible and sharpest in only one or two slicesof the 3D reconstructed data, which can be part of a large volume ofslices. Thus, the viewer often must review all slices or slabs in thedata set. When the data is projected onto a 2D projection usingtraditional methods, structures that exist above or below the object(mass) tends to obstruct the view, possibly occluding the mass, posing asignificant challenge in identifying such an object in the 2D projectionimage. However, if the system can effectively identify the region of themass before generating the 2D projection image, then the projectionprocess can be modified to ignore confusing structures above and belowthe mass to produce a much clearer view in the 2D projection. The endresult is a 2D projection in which the masses are also clearly visible,and generally free of any obstructions that could occlude a clear viewof the object (mass) of interest. Advantageously, it is contemplatedthat this illustrative process can also be adapted and applied tospiculated masses and calcifications in a manner clear to those ofskill.

Illustratively, the process can operate to first identifies the objectof interest in the 3D data, determines the best slice(s) that revealthis object, segments and extracts the region, and then smoothly mergesthe result with the traditional 2D projection.

The difference between a 2D-projected image before and after processingaccording to the illustrative system and method is shown in therespective exemplary display images 400 and 500 of FIGS. 4 and 5. Theseimages are close-up views of a region of interest containing an objectof interest (a suspected tumor and/or mass) in the center of the image.As shown in the display image 400 of FIG. 4 the object of interest 410is fuzzy and contains poorly defined (not sharp) boundaries, renderingit sometimes challenging to identify without close study of the images.Conversely, the exemplary display image 500 of FIG. 5, which is aprojected 2D image that has undergone the process of the illustrativesystem and method, displays the object of interest 510 withmore-defined, sharp boundaries. This renders the object 510 more-readilyidentified by a viewer, thereby increasing diagnostic accuracy,efficiency and throughput.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments of the apparatus and method of the presentinvention, what has been described herein is merely illustrative of theapplication of the principles of the present invention. For example,additional image handling algorithms/processes can be included in theoverall system process to enhance or filter image informationaccordingly. Accordingly, this description is meant to be taken only byway of example, and not to otherwise limit the scope of this invention.

What is claimed is:
 1. A system for processing image data relative to animaged anatomical region comprising: a. an acquisition process thatacquires one or more two-dimensional (2D) regions of interest (ROIs)from a three-dimensional (3D) medical image of the anatomical regionobtained from a medical imaging device; b. a first projection processthat defines a first 2D projection image of the anatomical region; c. asecond projection process that generates a second 2D projection image ofthe anatomical region using image information from the first 2Dprojection image and the one or more 2D ROIs; and d. at least one of adisplay and a data storage arrangement receiving an output of the second2D projection image.